Graph and Distributed Extensions of the Douglas–Rachford Method

IF 2.6 1区 数学 Q1 MATHEMATICS, APPLIED SIAM Journal on Optimization Pub Date : 2024-04-24 DOI:10.1137/22m1535097
Kristian Bredies, Enis Chenchene, Emanuele Naldi
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Abstract

SIAM Journal on Optimization, Volume 34, Issue 2, Page 1569-1594, June 2024.
Abstract. In this paper, we propose several graph-based extensions of the Douglas–Rachford splitting (DRS) method to solve monotone inclusion problems involving the sum of [math] maximal monotone operators. Our construction is based on the choice of two nested graphs, to which we associate a generalization of the DRS algorithm that presents a prescribed structure. The resulting schemes can be understood as unconditionally stable frugal resolvent splitting methods with minimal lifting in the sense of Ryu [Math. Program., 182 (2020), pp. 233–273] as well as instances of the (degenerate) preconditioned proximal point method, which provides robust convergence guarantees. We further describe how the graph-based extensions of the DRS method can be leveraged to design new fully distributed protocols. Applications to a congested optimal transport problem and to distributed support vector machines show interesting connections with the underlying graph topology and highly competitive performances with state-of-the-art distributed optimization approaches.
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道格拉斯-拉赫福德方法的图和分布式扩展
SIAM 优化期刊》,第 34 卷第 2 期,第 1569-1594 页,2024 年 6 月。 摘要本文提出了 Douglas-Rachford 分裂(DRS)方法的几种基于图的扩展,以解决涉及 [math] 最大单调算子之和的单调包含问题。我们的构造基于两个嵌套图的选择,我们将 DRS 算法的广义化与这两个嵌套图关联起来,从而呈现出一种规定的结构。由此产生的方案可以理解为无条件稳定的、具有 Ryu [Math. Program.我们进一步介绍了如何利用 DRS 方法基于图的扩展来设计新的全分布式协议。对拥挤的最优运输问题和分布式支持向量机的应用显示了与底层图拓扑的有趣联系,以及与最先进的分布式优化方法相比极具竞争力的性能。
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来源期刊
SIAM Journal on Optimization
SIAM Journal on Optimization 数学-应用数学
CiteScore
5.30
自引率
9.70%
发文量
101
审稿时长
6-12 weeks
期刊介绍: The SIAM Journal on Optimization contains research articles on the theory and practice of optimization. The areas addressed include linear and quadratic programming, convex programming, nonlinear programming, complementarity problems, stochastic optimization, combinatorial optimization, integer programming, and convex, nonsmooth and variational analysis. Contributions may emphasize optimization theory, algorithms, software, computational practice, applications, or the links between these subjects.
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